Occupancy map-based low complexity motion prediction for video-based point cloud compression

This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). We propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion region in common blocks. [Display omitted] •The attribute maps are divided into static, complex, and common blocks.•An early termination algorithm is proposed for static blocks.•An adaptive motion search range method is proposed for complex blocks.•An early inter prediction mode decision algorithm is proposed for common block. This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). Firstly, we propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion regions in common blocks. Experiment results show that, in comparison to the test model category2 (TMC2) v15.0, called the anchor method, the average bitrate savings of Y, U, and V components of the proposed method achieve 24.27%, 32.64%, and 31.23% on 8i voxelized full bodies version 2 (8iVFBv2) dataset, respectively. Further, the time savings is 41.97% for attribute maps. Similarly, the proposed method also achieves consistent performance on Microsoft voxelized upper bodies (MVUB) dataset.